Customer clustering using integer programming

ABSTRACT

Methods and apparatus are disclosed regarding an e-commerce system that clusters customers based on demographic data and purchase history data for the customers. In some embodiments, the e-commerce system solves an Integer Program that accounts for the demographic data and purchase history data in order to identify a hyperplane that splits a selected cluster of customers.

CLAIM OF BENEFIT

This patent application is a continuation of U.S. patent applicationSer. No. 16/366,542, filed Mar. 27, 2019, which is a continuation ofU.S. patent application Ser. No. 14/084,903, filed on Nov. 20, 2013. Theabove identified applications are hereby incorporated herein byreference in their entireties.

FIELD OF THE INVENTION

Various embodiments relate to electronic commerce (e-commerce), and moreparticularly, to classifying customers in an e-commerce environment.

BACKGROUND OF THE INVENTION

Electronic commerce (e-commerce) websites are an increasingly popularvenue for consumers to research and purchase products without physicallyvisiting a conventional brick-and-mortar retail store. An e-commercewebsite may provide products and/or services to a vast number ofcustomers. As a result of providing such products and/or services, thee-commerce website may obtain extensive amounts of data about theircustomer base. Such customer data may aid the e-commerce website toprovide products and/or services that are relevant and/or otherwisedesirable to a particular customer.

In particular, an e-commerce website may attempt to identify groups ofcustomers with similar interests or similar lifestyles. The e-commercewebsite may analyze these identified groups to derive generalizationsregarding members of the group. The e-commerce website may then tailorits services to members of each group based upon the derivedgeneralizations.

Limitations and disadvantages of conventional and traditional approachesshould become apparent to one of skill in the art, through comparison ofsuch systems with aspects of the present invention as set forth in theremainder of the present application.

BRIEF SUMMARY OF THE INVENTION

Apparatus and methods of classifying or grouping customers aresubstantially shown in and/or described in connection with at least oneof the figures, and are set forth more completely in the claims.

These and other advantages, aspects and novel features of the presentinvention, as well as details of an illustrated embodiment thereof, willbe more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 shows an e-commerce environment comprising a computing device andan e-commerce system in accordance with an embodiment of the presentinvention.

FIG. 2 shows an embodiment of a computing device for use in thee-commerce environment of FIG. 1.

FIG. 3 shows user profiles and product catalogs maintained by ane-commerce system of FIG. 1.

FIG. 4 shows an embodiment of a product listing provided by thee-commerce system of FIG. 1.

FIG. 5 shows a flowchart for an embodiment of a process that may be usedby the e-commerce system of FIG. 1 to obtain a transaction space and afeature space from purchase history data and demographic data.

FIG. 6 shows an example entry of the purchase history data for thee-commerce system of FIG. 1.

FIG. 7 shows an example purchase history table for the e-commerce systemof FIG. 1 after evaluating and retaining data of the purchase historydata for a time window of interest.

FIG. 8 shows an example purchase history table for the e-commerce systemof FIG. 1 after combining rows that correspond to the same customer andproduct category.

FIG. 9 shows an entry from an example customer-item (CI) matrix for thee-commerce system of FIG. 1.

FIG. 10 shows an example quantile table for the e-commerce system ofFIG. 1.

FIG. 11 shows a standardized entry from the example quantile table ofFIG. 10.

FIG. 12 shows a flowchart of a process that may be used by thee-commerce system of FIG. 1 to cluster customers based on thetransaction space and feature space.

FIGS. 13-16 depict an example partitioning of a customer base.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the present invention are related to classifying and/orgrouping customers together that exhibit similar interests, lifestyles,and/or purchase behavior. More specifically, certain embodiments of thepresent invention relate to apparatus, hardware and/or software systems,and associated methods that cluster customers based on solving anInteger Program that accounts for purchase history data and demographicdata of the customers.

Referring now to FIG. 1, an e-commerce environment 10 is depicted. Asshown, the e-commerce environment 10 may include a computing device 20connected to an e-commerce system 30 via a network 40. The network 40may include a number of private and/or public networks such as, forexample, wireless and/or wired LAN networks, cellular networks, and theInternet that collectively provide a communication path and/or pathsbetween the computing device 20 and the e-commerce system 30. Thecomputing device 20 may include a desktop, a laptop, a tablet, a smartphone, and/or some other type of computing device which enables a userto communicate with the e-commerce system 30 via the network 40. Thee-commerce system 30 may include one or more web servers, databaseservers, routers, load balancers, and/or other computing and/ornetworking devices that operate to provide an e-commerce experience forusers that connect to the e-commerce system 30 via the computing device20 and the network 40.

The e-commerce system 30 may further include a customer classifier 33,one or more tailored services 35, and one or more electronic databases37 upon which are stored purchase history data 38 and demographic data39 for customers of the e-commerce system 30. The classifier 33 mayinclude one or more firmware and/or software instructions, routines,modules, etc. that the e-commerce system 30 may execute in order toclassify, group, or cluster customers of the e-commerce system 30 intoclasses, groups, or clusters of customers that exhibit similarpurchasing habits. The classifier 33 may analyze purchase history dataand demographic data for the customers to identify clusters of customerswith similar purchasing preferences.

The tailored services 35 may comprise one or more firmware and/orsoftware instructions, routines, modules, etc. that the e-commercesystem 30 may execute in order to tailor one or more aspects of thee-commerce system 30 for a particular customer. The tailored services 35may include advertisements, promotions, product recommendations, emailcampaigns, etc. that are tailored based upon the cluster to which thecustomer has been placed.

The classifier 33 and tailored services 35 may be executed concurrentlyby a single computing device of the e-commerce system 30. However, insome embodiments, a computing device may execute the classifier 33offline in order to obtain appropriate clusters and other input data forthe tailored services 35. Moreover, the classifier 33 may periodically(e.g., once an hour, once a day, once a week, etc.) provide one or moreof the tailored services 35 with updated cluster and other input data.In this manner, the e-commerce system 30 may continue to providetailored services 35 without the constant overhead of the classifier 33and/or without the overhead of constant updates. For example, thee-commerce system 30 may execute the classifier 33 only during generallyidle periods (e.g., after normal business hours). Further detailsregarding the classifier 33 and the tailored services 35 are presentedbelow in regard to FIGS. 5-11.

FIG. 1 depicts a simplified embodiment of the e-commerce environment 10which may be implemented in numerous different manners using a widerange of different computing devices, platforms, networks, etc.Moreover, while aspects of the e-commerce environment 10 may beimplemented using a client/server architecture, aspects of thee-commerce may be implemented using a peer-to-peer architecture oranother networking architecture.

As noted above, the e-commerce system 30 may include one or morecomputing devices. FIG. 2 depicts an embodiment of a computing device 50suitable for the computing device 20 and/or the e-commerce system 30. Asshown, the computing device 50 may include a processor 51, a memory 53,a mass storage device 55, a network interface 57, and variousinput/output (I/O) devices 59. The processor 51 may be configured toexecute instructions, manipulate data and generally control operation ofother components of the computing device 50 as a result of itsexecution. To this end, the processor 51 may include a general purposeprocessor such as an x86 processor or an ARM processor which areavailable from various vendors. However, the processor 51 may also beimplemented using an application specific processor and/or other logiccircuitry.

The memory 53 may store instructions and/or data to be executed and/orotherwise accessed by the processor 51. In some embodiments, the memory53 may be completely and/or partially integrated with the processor 51.

In general, the mass storage device 55 may store software and/orfirmware instructions which may be loaded in memory 53 and executed byprocessor 51. The mass storage device 55 may further store various typesof data which the processor 51 may access, modify, and/otherwisemanipulate in response to executing instructions from memory 53. To thisend, the mass storage device 55 may comprise one or more redundant arrayof independent disks (RAID) devices, traditional hard disk drives (HDD),solid-state device (SSD) drives, flash memory devices, read only memory(ROM) devices, etc.

The network interface 57 may enable the computing device 50 tocommunicate with other computing devices directly and/or via network 40.To this end, the networking interface 57 may include a wired networkinginterface such as an Ethernet (IEEE 802.3) interface, a wirelessnetworking interface such as a WiFi (IEEE 802.11) interface, a radio ormobile interface such as a cellular interface (GSM, CDMA, LTE, etc),and/or some other type of networking interface capable of providing acommunications link between the computing device 50 and network 40and/or another computing device.

Finally, the I/O devices 59 may generally provide devices which enable auser to interact with the computing device 50 by either receivinginformation from the computing device 50 and/or providing information tothe computing device 50. For example, the I/O devices 59 may includedisplay screens, keyboards, mice, touch screens, microphones, audiospeakers, etc.

While the above provides general aspects of a computing device 50, thoseskilled in the art readily appreciate that there may be significantvariation in actual implementations of a computing device. For example,a smart phone implementation of a computing device may use vastlydifferent components and may have a vastly different architecture than adatabase server implementation of a computing device. However, despitesuch differences, computing devices generally include processors thatexecute software and/or firmware instructions in order to implementvarious functionality. As such, aspects of the present application mayfind utility across a vast array of different computing devices and theintention is not to limit the scope of the present application to aspecific computing device and/or computing platform beyond any suchlimits that may be found in the appended claims.

As part of the provided e-commerce experience, the e-commerce system 30may enable customers, which may be guests or members of the e-commercesystem 30, to browse and/or otherwise locate products. The e-commercesystem 30 may further enable such customers to purchase products offeredfor sale. To this end, the e-commerce system 30 may maintain anelectronic product database or product catalog 300 which may be storedon an associated mass storage device 55. As shown in FIG. 3, the productcatalog 300 includes product listings 310 for each product available forpurchase. Each product listing 310 may include various information orattributes regarding the respective product, such as a unique productidentifier (e.g., stock-keeping unit “SKU”), a product description,product image(s), manufacture information, available quantity, price,product features, etc. Moreover, while the e-commerce system 30 mayenable guests to purchase products without registering and/or otherwisesigning-up for a membership, the e-commerce system 30 may provideadditional and/or enhanced functionality to those users that become amember.

To this end, the e-commerce system 30 may enable members to create acustomer profile 330. As shown, a customer profile 330 may includepersonal information 331, purchase history data 335, and other customeractivity data 337. The personal information 331 may include such itemsas name, mailing address, email address, phone number, billinginformation, clothing sizes, birthdates of friends and family, etc. Thepurchase history data 335 may include information regarding productspreviously purchased by the customer from the e-commerce system 30. Thecustomer history data 335 may further include products previouslypurchased from affiliated online and brick-and-mortar vendors.

The other customer activity data 337 may include information regardingprior customer activities such as products for which the customer haspreviously searched, products for which the customer has previouslyviewed, products for which the customer has provide comments, productsfor which the customer has rated, products for which the customer haswritten reviews, etc. and/or purchased from the e-commerce system 30.The other customer activity data 337 may further include similaractivities associated with affiliated online and brick-and-mortarvendors.

As part of the e-commerce experience, the e-commerce system 30 may causea computing device 10 to display a product listing 310 as shown in FIG.4. In particular, the e-commerce system 30 may provide such a productlisting 310 in response to a member browsing products by type, price,kind, etc., viewing a list of products obtained from a product search,and/or other techniques supported by the e-commerce system 30 forlocating products of interest. As shown, the product listing 310 mayinclude one or more representative images 350 of the product as well asa product description 360. The product listing 310 may further includeone or more products 370 recommended by a recommendation engine of thetailored services 35. In particular, the recommendation engine mayprovide product recommendations based on the personal information 331,purchase history data 335 and/or activity data 337.

Referring now to FIG. 5, an example method 500 that may be implementedby the classifier 33 of the e-commerce system 30 is shown. In general,the classifier 33 in accordance with the method 500 respectivelytransforms the purchase history data and demographic data into atransaction space and feature space which the classifier 33 may use topartition or cluster the customer base as shown and discussed below inregard to FIG. 6. To this end, the classifier 33 at 510 may preprocesspurchase history data 335 to obtain a Customer-Item (CI) matrix. Thee-commerce system 30 may collect and maintain purchase history data 335for the customer over a period of time. The purchase history data, inits raw form, may include information recorded for each purchase. Anexample entry is shown in FIG. 6. As shown, the e-commerce system 30 maymaintain the purchase history data 335 in one or more relationaldatabase tables. Each row of the purchase history table may include arow for each transaction, and each row may include a customer identifier(ID) that uniquely identifies the customer associated with thecorresponding transaction.

At 510, the classifier 33 may preprocess the raw purchase historyinformation found in the purchase history table into a Customer-Itemspace. To this end, the classifier 33 may select a time window (e.g.,the most recent 24 months). The classifier 33 may extract entries fromthe purchase history table that have a transaction date that fallswithin the selected time window. The classifier 33 may then discard allfields other than the Customer ID, Item ID and Quantity of thatparticular item purchased in that transaction.

Many e-commerce sites maintain a product hierarchy of productidentifiers where the Item ID corresponds to the lowest level of suchhierarchy and various Category IDs lie higher up in the producthierarchy. Moreover, in many environments, the Item IDs are at such afine a granularity that correlations between purchases may be lost. Insuch situations, the classifier 33 may be configured to coalescepurchased items of multiple Item IDs under a single Category ID thatlies at a high level in the product hierarchy.

FIG. 7 shows an example table after evaluating the time window asdescribed above. As may be seen from FIG. 7, the resulting table maystill include multiple entries or rows for each Customer ID and CategoryID pair. The classifier 33 may apply a pivoting step to the resultingtable in order to combine rows having the same Customer ID and CategoryID pair into a single row. As shown in FIG. 8, the resulting tableincludes a single row for each Customer ID and Category ID pair andincludes Quantity data that contains the sum of all purchased quantitiesfor this ID pair.

From the table shown in FIG. 8, the classifier 33 may create aCustomer-Item (CI) matrix. In the CI matrix, each row i corresponds to aunique Customer ID, each column j corresponds to a unique Category ID,and the entry CI_(ij) corresponds to the quantity of this Customer IDand Category ID pair from the table shown in FIG. 7. If a particularcustomer did not purchase from a product in a category of CI matrix,then corresponding entry is zero.

At 515, the classifier 33 may further preprocess the demographic data ofits customers to obtain a feature space. The e-commerce system 30 maycollect demographic data from customers such as personal information 331provided in the customers profile 330. The e-commerce system 30 mayfurther obtain demographic data for customers from various providers ofdemographic data. Based on such collected demographic data, theclassifier 33 may maintain and/or create a demographic table. Thedemographic table may include a row for each Customer ID. Moreover, eachcolumn of the table may represent a different feature such, as forexample, age, gender, occupation, number of children, etc. Duringpreprocessing, the classifier 33 may turn each demographic entry into anumerical value. For example, the “Gender” column may contain only twokinds of entries, male and female. The classifier 33 may preprocess thedemographic table such that that Gender column includes a 1 for eachfemale customer and a 0 otherwise. The preprocessed demographic tablemay form the feature space for later classification.

After preprocessing the purchase history and demographic data, theclassifier 33 at 520 may standardize the CI matrix to obtain astandardized CI matrix which is referred to as transaction space.Standardizing the CI Matrix may ensure that the columns of thestandardized CI matrix are scale-wise comparable with each other. In oneembodiment, the classifier 33 applies standardization to each columnseparately using a bin quantiles standardization (BQS) technique.However, other standardization techniques may be utilized.

To illustrate the BQS technique, one example column of the CI matrix isshown in FIG. 9. If depicted column corresponds to a category ID CID inthe CI matrix, then the information in column suggests that customer 1bought 1 unit of an item corresponding to category ID, customer 4 bought2 items, customer 6 bought 1 item, and customer 7 bought 8 items. Theclassifier 33 in accordance with the BQS technique may traverse thecolumn, record every unique quantity except zero that appears along withhow many times each unique quantity appears in the column. Theclassifier 33 may sort the results based on occurrence of each uniquequantity. See, e.g., the Occurrences column of FIG. 10. The classifier33 may traverse the occurrences to obtain a cumulative sum of the numberof occurrences. See, e.g., Cumulative Occurrences column of FIG. 10.Furthermore, the classifier 33 for each row may divide the respectivecumulative occurrence value by the last number in the cumulativeoccurrence column (i.e., the total number of occurrences) to obtain thequantile value for that row. See, e.g., Quantile column of FIG. 10.

The BQS result shown in FIG. 10 suggests that the customers who bought 1item associated with the category ID constitute the first 50% quantile,customers who bought 2 or less such items are the 75% quantile, andcustomers who bought 8 or less such items are the 100% quantile. Theclassifier 33 may then update the quantity values of the original columnwith their corresponding quantile values as shown in FIG. 11 to obtainthe standardized column.

The BQS technique may provide two advantages. One, all the numbers inthe columns of CI matrix are guaranteed to be between 0 and 1, thereforethe purchase patterns of high-frequency items such as grocery items anda low-frequency items such as expensive electronics items arecomparable. Second, because the quantile values are thought in terms offrequencies of each number appearing and their relative order ratherthan their nominal values, the occasional very large number observed inthe columns do not skew the analysis.

After obtaining feature space the standardized transaction spaces, theclassifier 33 may classify or cluster the customers. In particular, theclassifier 33 may attempt to find linear partitions in the feature spacethat divides the data points (customers) into groups or clusters withthe smallest sum of distances within themselves. The distances aredefined using the standardized transaction space.

The distance between customer A and customer B is a measure of thedissimilarity between their purchase history data 335. While manydistance functions may be used, the classifier 33 in one embodiment usesthe Minkowski distance for Euclidean space. The Minkowski distance foran integer p may be represented by the following expression:

(Σ_(i=1) ^(n) |CI _(A) ^(i)-CI _(B) ^(i)|^(p))^(1/p)

where CI_(A) represents the row in the standardized CI matrix for thecustomer A; CI_(B) represents the row in the standardized CI matrix forthe customer B; CI_(A) ^(i) represents the i^(th) element of row CI_(A);CI_(B) ^(i) represents the i^(th) element of row CI_(B). The cases wherep=1 and p=2 correspond to the Manhattan distance and Euclidean distance,respectively.

The classifier 33 may alternatively utilize a distance function thatprovides a metric of the similarity between customers. In such anembodiment, the classifier 33 may attempt to maximize the sum ofinner-similarities per cluster. For example, the classifier 33 may useJaccard similarity functions, correlation functions, and/or some othersimilarity function in such an embodiment.

After obtaining the feature space and transaction space, the classifier33 may proceed to analyze the feature space and transaction space inorder to identify clusters of customers with similar purchasingbehaviors. To this end, the classifier 33 may iteratively dividecustomer sets into two partitions until a suitable number of partitionsfor the customer base is obtained. In particular, the classifier 33 maydivide the feature space into two partitions that minimizes theinner-distance between members of the cluster in the transaction spaceby solving an Integer Program that takes into account both the featurespace and transaction space of the customer base.

In one embodiment, the following parameters, data, variables, andformulation define a Integer Program which may be solved to obtain ahyperplane that suitably divides the customer base into two clusters.

Parameters and Data:

-   -   n=number of customers;    -   m=number of dimensions in feature space;    -   x_(i)=length-m coordinate vector of customer i in feature space        for i=1 . . . n;    -   d_(ij)=distance between customers i and j in transaction space        according to a pre-selected distance metric;    -   C=a large constant; and    -   ε=a small constant (epsilon).

Variables:

-   -   I_(i)=indicator variable of customer i, which is one if customer        i is in cluster 1 (one side of the optimum hyperplane), and zero        if the customer is in cluster 2 (the other side of the        hyperplane) in the feature space.    -   J_(ij)=indicator variable for customer pair (i,j), which is        equal to one if i and j are in the same cluster, and zero if        they are in different clusters.    -   β=the length-m direction vector in feature space that defines        the direction of the dividing hyperplane.    -   β₀=scalar intercept of the dividing hyperplane.

Formulation:

${Minimize}\text{:}{\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{n}{d_{ij}J_{ij}}}}$

Subject to:

βx _(i)+β₀≤(1−I _(i))•C∀i

−βx _(i)−β₀ ≤I _(i) •C∀−ε∀i

I _(i) −I _(j)≤1−J _(ij) ∀i,j

I _(i) +I _(j)≤1−J _(ij) ∀i,j

I _(i)∈{0,1}∀i

0≤J _(ij)≤1 ∀i,j

The above Integer Program, when solved by an Integer Programming solverof the classifier 33, returns the clustering of customers in the featurespace together with hyperplane variables β and β₀ that define thedivision rule for the clusters. The classifier 33 may use the divisionrule to place new customers into one of the defined clusters based onknown demographic features. By doing so, the classifier 33 may obtainsome insight into the likely purchasing behavior for a new customerdespite not having much or any purchase history data for the newcustomer.

The above Integer Program, however, divides the customer base into onlytwo clusters or partitions, which is most likely not enough number ofclusters to provide meaningful insight into the purchasing behaviors ofthe customer base. Accordingly, the classifier 33 may iteratively applythe above Integer Program in order to further divide the clusters untila suitable number of clusters are obtained. Such an iterative clusteringmethod 600 is shown in FIG. 12.

At 610, the classifier 33 at 610 may solve the above Integer Program toobtain a hyperplane that divides or partitions the customer base or dataset into two partitions or clusters. After dividing the data set intotwo clusters, the classifier 33 at 620 may determine whether furtherpartitioning of the data set is warranted. To this end, the classifier33 may make such a determination based upon a stopping rule. A stoppingrule may define conditions for stopping further partitioning of the dataset and for identifying which cluster or clusters to further divide. Afirst example stopping rule may be to pre-define the desired number ofclusters, and iteratively keep dividing the cluster with the largestpopulation until the desired number of clusters is reached. A secondexample stopping rule may be to define the largest population to beallowed in a single cluster, and keep dividing the clusters that aremore populated than this limit until no cluster exceeds this limit. Itshould be appreciated that the above two stopping rules are merelyexamples and that other stopping rules and/or a combination of rules maybe used by the classifier 33 to ascertain whether to cease partitioningand/or selecting which clusters to further partition.

If the classifier 33 determines that no further partitioning iswarranted, then the classifier 33 may cease further partitioning of thedata set. However, if the classifier 33 determines that the stoppingrules indicates further partitioning is warranted, then the classifier33 at 630 may select a cluster for further partitioning based on thestopping rule. For example, the classifier 33 per the first examplestopping rule may select the cluster having the largest population forfurther partitioning. If the second example stopping rule is being used,then the classifier 33 may select a cluster having a population greaterthan the predefined limit.

After selecting an appropriate cluster for further partitioning, theclassifier 33 may return to 610 in order to solve the Integer Programand obtain a hyperplane that partitions the selected cluster into twosmaller clusters. In this manner, the classifier 33 may continue toobtain further partitions until a suitable number of partitions isachieved per the stopping rule in effect.

Referring now to FIGS. 13-16, an example of partitioning a data set ofcustomers per the method 600 is shown. In particular, the exampleillustrates partitioning based on a stopping rule of the largestallowable cluster having a population of 3. Starting with FIG. 13, anunclustered data set of 9 customers in a two dimensional feature spaceis shown. FIG. 14 shows a hyperplane H₁ obtained by the classifier 33 asa result of solving the Integer Program in order to partition the 9customers of FIG. 13. After such partitioning of FIG. 14, the lowerpartition has a data set of 3 customers and is thus not divided furtherper the stopping rule. The upper partition, however, defines a data setof 6 customers and thus exceeds the population limit of 3 for thestopping rule. As such, the classifier solves the Integer Program forthe upper data set to obtain the hyperplane H₂ shown in FIG. 15.

After such partitioning of FIG. 15, the upper left partition has a dataset of 2 customers and is thus not divided further per the stoppingrule. The upper right partition, however, defines a data set of 4customers and thus still exceeds the population limit of 3 for thestopping rule. As such, the classifier solves the Integer Program forthe upper right data set to obtain the hyperplane H₃ shown in FIG. 16.After such partitioning of FIG. 16, all partitions have less the thanpopulation limit of 3. As such, the classifier 33 ceases furtherpartitioning of the customer base per the stopping rule.

Various embodiments of the invention have been described herein by wayof example and not by way of limitation in the accompanying figures. Forclarity of illustration, exemplary elements illustrated in the figuresmay not necessarily be drawn to scale. In this regard, for example, thedimensions of some of the elements may be exaggerated relative to otherelements to provide clarity. Furthermore, where considered appropriate,reference labels have been repeated among the figures to indicatecorresponding or analogous elements.

Moreover, certain embodiments may be implemented as a plurality ofinstructions on a non-transitory, computer readable storage medium suchas, for example, flash memory devices, hard disk devices, compact discmedia, DVD media, EEPROMs, etc. Such instructions, when executed by oneor more computing devices, may result in the one or more computingdevices identifying customer clusters based on purchase history data anddemographic data for the customer.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. For example, theabove embodiments were described primarily from the standpoint of ane-commerce environment. However, it should be appreciated thatclustering of customers may be useful in other environments as well. Forexample, a brick-and-mortar store may cluster customers in order toprovide targeted mailing, coupons, and/or other types of promotions toits customers. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentinvention without departing from its scope. Therefore, it is intendedthat the present invention not be limited to the particular embodimentor embodiments disclosed, but that the present invention encompasses allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A method comprising: providing a service with afirst computing system, wherein the first computing system tailors theservice to a customer based on a customer cluster from a plurality ofcustomer clusters in which the customer resides; periodically updating,via a classifier of a second computing system, the plurality of customerclusters based on purchase history data and demographic data for aplurality of customers; and providing the first computing system withthe plurality of customer clusters updated by the classifier of thesecond computing system to permit the first computing system to continueto provide the service without incurring an overhead associated withprocessing of the purchase history data and demographic data by theclassifier of the second computing system.
 2. The method of claim 1,wherein providing the service with the first computing system comprisesproviding product recommendations based on the cluster in which thecustomer resides.
 3. The method of claim 1, wherein providing theservice with the first computing system comprises providing productpromotions based on the cluster in which the customer resides.
 4. Themethod of claim 1, wherein providing the service with the firstcomputing system comprises providing coupons based on the cluster inwhich the customer resides.
 5. The method of claim 1, wherein updatingvia the classifier of the second computing system comprises solving anInteger Program that accounts for the purchase history data anddemographic data of a selected cluster.
 6. The method of claim 1,wherein updating via the classifier of the second computing systemcomprises selecting a cluster that has a population greater than aspecified limit and splitting the cluster.
 7. The method of claim 1,further comprising storing the purchase history data in one or morerelational database tables such that each row includes transaction dataand a customer identifier that identifies a customer associated withtransaction data.
 8. The method of claim 1, wherein said updating viathe classifier of the second computing system comprises coalescingpurchased items of multiple item identifiers under a single identifierand updating the plurality of customer clusters based on the purchaseditems un the single identifier.
 9. The method of claim 1, wherein saidupdating via the classifier of the second computing system comprisesupdating the plurality of customer clusters based on a customer-item(CI) matrix, wherein each row of corresponds to a customer identifier,each column corresponds to a category identifier, and each entrycorresponds to a quantity associated with the customer identifier,category identifier pair.
 10. The method of claim 9, wherein saidupdating via the classifier of the second computing system comprisesseparately standardizing each column of CI matrix using a bin quantilesstandardization (BQS) technique.
 11. A system for providing a service toa customer, the system comprising: a first computing system configuredto tailor the service for the customer based on a customer cluster froma plurality of customer clusters in which the customer resides; and asecond computing system comprising a classifier configured toperiodically update the plurality of customer clusters based on purchasehistory data and demographic data for a plurality of customers; whereinthe second computing system is configured to provide the first computingsystem with the plurality of customer clusters as updated by theclassifier; and wherein the first computing system is configured toprovide the service, per the plurality of customer clusters as updatedby the classifier, without incurring an overhead associated with theclassifier processing of the purchase history data and demographic data.12. The system of claim 11, wherein the first computing system isconfigured to tailor the service by providing product recommendationsbased on the cluster in which the customer resides.
 13. The system ofclaim 11, wherein the first computing system is configured to tailor theservice by providing product promotions based on the cluster in whichthe customer resides.
 14. The system of claim 11, wherein the firstcomputing system is configured to tailor the service by providingcoupons based on the cluster in which the customer resides.
 15. Thesystem of claim 11, wherein the second computing system is configured toupdate the plurality of customer clusters by solving an Integer Programthat accounts for the purchase history data and demographic data of aselected cluster.
 16. The system of claim 11, wherein the secondcomputing system is configured to update the plurality of customerclusters by selecting a cluster that has a population greater than aspecified limit and splitting the cluster.
 17. The system of claim 11,wherein the second computing system is further configured to access thepurchase history data from one or more relational database tables,wherein each row includes transaction data and a customer identifierthat identifies a customer associated with transaction data.
 18. Thesystem of claim 11, wherein the second computing system is furthercoalesce purchased items of multiple item identifiers under a singleidentifier and update the plurality of customer clusters based on thepurchased items under the single identifier.
 19. The system of claim 11,wherein the second computing system is further configured to form acustomer-item (CI) matrix, wherein each row of corresponds to a customeridentifier, each column corresponds to a category identifier, and eachentry corresponds to a quantity for the associated with the customeridentifier, category identifier pair.
 20. The system of claim 19,wherein the second computing system is further configured to separatelystandardize each column of CI matrix using a bin quantilesstandardization (BQS) technique.